Scipediacontent: Scipediacontent moved page Draft Content 734727713 to Papachristou et al 2018a

2021-02-15T09:17:45Z

Scipediacontent moved page Draft Content 734727713 to Papachristou et al 2018a

Scipediacontent: Created page with " == Abstract == Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the..."

2021-02-15T09:17:42Z

Created page with " == Abstract == Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the..."

New page

== Abstract ==

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_734727713-beopen697-7908-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1371/journal.pone.0208808 http://dx.doi.org/10.1371/journal.pone.0208808] under the license http://creativecommons.org/licenses/by/4.0

* [http://epubs.surrey.ac.uk/849947 http://epubs.surrey.ac.uk/849947] under the license http://creativecommons.org/licenses/by/4.0

* [http://europepmc.org/articles/PMC6312306 http://europepmc.org/articles/PMC6312306]

* [http://hdl.handle.net/10044/1/83131 http://hdl.handle.net/10044/1/83131]

* [https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208808&type=printable https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208808&type=printable] under the license http://creativecommons.org/licenses/by/4.0/

* [https://strathprints.strath.ac.uk/66580/1/Papachristou_etal_POS2018_Learning_from_data_to_predict_future_symptoms_of_oncology.pdf https://strathprints.strath.ac.uk/66580/1/Papachristou_etal_POS2018_Learning_from_data_to_predict_future_symptoms_of_oncology.pdf] under the license cc-by

* [https://zenodo.org/record/3368254 https://zenodo.org/record/3368254] under the license http://creativecommons.org/licenses/by/4.0

* [http://dx.plos.org/10.1371/journal.pone.0208808 http://dx.plos.org/10.1371/journal.pone.0208808],
: [http://dx.doi.org/10.1371/journal.pone.0208808 http://dx.doi.org/10.1371/journal.pone.0208808] under the license http://creativecommons.org/licenses/by/4.0/legalcode

* [https://doi.org/10.1371/journal.pone.0208808 https://doi.org/10.1371/journal.pone.0208808],
: [https://doaj.org/toc/1932-6203 https://doaj.org/toc/1932-6203] under the license http://creativecommons.org/licenses/by/4.0/

* [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208808 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208808],
: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312306 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312306],
: [http://epubs.surrey.ac.uk/849947 http://epubs.surrey.ac.uk/849947],
: [https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208808&type=printable https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208808&type=printable],
: [https://strathprints.strath.ac.uk/66580 https://strathprints.strath.ac.uk/66580],
: [http://ui.adsabs.harvard.edu/abs/2018PLoSO..1308808P/abstract http://ui.adsabs.harvard.edu/abs/2018PLoSO..1308808P/abstract],
: [https://nyuscholars.nyu.edu/en/publications/learning-from-data-to-predict-future-symptoms-of-oncology-patient https://nyuscholars.nyu.edu/en/publications/learning-from-data-to-predict-future-symptoms-of-oncology-patient],
: [https://pureportal.strath.ac.uk/en/publications/learning-from-data-to-predict-future-symptoms-of-oncology-patient https://pureportal.strath.ac.uk/en/publications/learning-from-data-to-predict-future-symptoms-of-oncology-patient],
: [http://europepmc.org/articles/PMC6312306 http://europepmc.org/articles/PMC6312306],
: [https://dx.plos.org/10.1371/journal.pone.0208808 https://dx.plos.org/10.1371/journal.pone.0208808],
: [https://academic.microsoft.com/#/detail/2903477703 https://academic.microsoft.com/#/detail/2903477703]

← Older revision	Revision as of 09:17, 15 February 2021
(No difference)

Papachristou et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 734727713 to Papachristou et al 2018a

Scipediacontent: Created page with " == Abstract == Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the..."